Agentic AI for Laboratories

We make your instrument speak the language your AI agents already understand

Lab orchestrators, AI copilots, and cloud platforms only work when each instrument speaks fluently. We build that layer - MCP servers and SiLA 2 drivers - on your instrument, in the languages your firmware runs, against the standards platforms consume. Source code yours from day one. Fixed price per engagement, scope you control.

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Explore Our Agentic AI Work

Two focus areas where we ship today - the MCP infrastructure layer and the workflow agents that run on top of it.

Infrastructure Layer

MCP Servers for Lab Instruments

Production-grade Model Context Protocol servers that expose instrument capabilities to any AI agent. SiLA 2 and vendor SDK adapters, schema validation, audit trails.

Workflow Agents

Agentic AI for Lab Automation

Workflow-level agents that orchestrate sample prep, plate handling, and instrument scheduling on top of your existing automation. Plan-simulate-execute with human approval. No platform lock-in.

Key Capabilities

Production-grade MCP infrastructure - not a prototype.

MCP Server Architecture

We build Model Context Protocol servers that expose instrument capabilities as structured tools. Any MCP-compatible AI agent can discover and invoke instrument operations - no custom integration required.

Natural Language Commands

Scientists describe what they need in plain English. The AI agent translates intent into validated instrument commands - handling parameter validation, safety checks, and error recovery automatically.

Human-in-the-Loop Guardrails

Every critical operation requires explicit human approval. Configurable safety policies, audit trails, and rollback mechanisms. The AI suggests - the scientist decides.

API Spec to MCP in Days

Have an instrument API spec (REST, gRPC, serial, proprietary)? We generate a production-grade MCP server from it. Schema validation, error handling, and observability included out of the box.

How It Works

From instrument API to AI-controlled lab in weeks, not months.

Instrument Discovery

We analyze your instrument APIs, communication protocols, and command structures. Serial, REST, gRPC, proprietary - we have seen it all.

MCP Server Generation

We build a Model Context Protocol server that wraps your instrument capabilities as structured tools with full schema validation and documentation.

Safety & Policy Layer

We configure human-in-the-loop approval flows, rate limits, parameter bounds, and audit logging. Every action is traceable and reversible.

Agent Integration

Connect any MCP-compatible AI agent - Claude, custom agents, or your existing LLM infrastructure. Scientists start controlling instruments through conversation.

Technical Specifications

ProtocolModel Context Protocol (MCP)

Transportstdio, SSE, WebSocket

AuthOAuth 2.0, API keys, mTLS

LanguagesPython, TypeScript, Rust, C++

Instrument ProtocolsREST, gRPC, serial, SCPI, proprietary

ObservabilityOpenTelemetry, structured logging

DeploymentDocker, on-premise, air-gapped

ComplianceAudit trails, 21 CFR Part 11 ready

Built on shipped instrument software

Two delivered references in our first 21 months - Lino Biotech AG (instrument control software, 18+ months ongoing) and Ridgeview Instruments AB (remote monitoring platform). One open-source contribution to the agentic-AI-for-instruments community (openapi-to-sila2 on PyPI, Apache 2.0). One working AI-agent prototype (LiquidBridge - Claude Desktop + MCP + digital twin for a liquid handler) demoed live in our first 30-minute call.

18+

months embedded with clients

delivered references with named testimonials

Apache 2.0

open-source contribution on PyPI

patient-impacting defects on shipped IVD work

Frequently Asked Questions

An MCP (Model Context Protocol) server exposes lab instrument capabilities as structured tools that any AI agent can discover and invoke. It translates natural language commands into validated instrument operations with safety checks and audit trails.

With an existing instrument API spec (REST, gRPC, serial, or proprietary), QPillars can deliver a production-grade MCP server in days to weeks, depending on protocol complexity and safety requirements.

Yes. QPillars builds human-in-the-loop guardrails into every MCP server. Every critical operation requires explicit human approval, with configurable safety policies, audit trails, and rollback mechanisms. The AI suggests - the scientist decides.

Make your instrument agent-native

30 minutes with the founder. We look at your instrument API and your customers' AI roadmap, then map a concrete deployment path - no templated decks, no slideware.

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